As used herein, the term "disease" is defined as a deviation from the normal structure or function of any part, organ or system of the body (or any combination thereof). A specific disease is manifested by characteristic symptoms and signs, including both chemical and physical changes. A disease is often associated with a variety of other factors including but not limited to demographic, environmental, employment, genetic and medically historical factors. Certain characteristic signs, symptoms, and related factors can be quantitated through a variety of methods to yield important diagnostic information. For purposes of this application, the quantifiable signs, symptoms and/or analytes in biological fluids characteristic of a particular disease are defined as "biomarkers" for the disease. Current diagnostic and prognostic methods depend on the identification and evaluation of these biomarkers, both individually and as they relate to one another. Often the diagnosis of a particular disease involves the subjective analysis by a clinician, such as a physician, veterinarian, or other health care provider, of the data obtained from the measurement of the factors mentioned above in conjunction with a consideration of many of the traditionally less quantitative factors such as employment history. Unfortunately, this subjective process of diagnosing or prognosing a disease usually cannot accommodate all the potentially relevant factors and provide an accurate weighting of their contribution to a correct diagnosis or prognosis.
Generally, the pathological process involves gradual changes that become apparent only when overt change has occurred. In many instances, pathological changes involve subtle alterations in multiple biomarkers. It is uncommon that a single biomarker will be indicative of the presence or absence of a disease. It is the pattern of those biomarkers relative to one another and relative to a normal reference range, that is indicative of the presence of a disease. Additional factors including but not limited to demographic, environmental, employment, genetic and medically historical factors may contribute significantly to the diagnosis or prognosis of a disease, especially when considered in conjunction with patterns of biomarkers. Unfortunately, the subjective diagnostic process of considering the multiple factors associated with the cause or presence of a disease is somewhat imprecise and many factors that may contribute significantly are not afforded sufficient weight or considered at all.
When individual biomarkers do not show a predictable change and the patterns and interrelationships among the biomarkers viewed collectively are not clear, the accuracy of a physician's diagnosis is significantly reduced. Also, as the number of biomarkers and demographic variables relevant for the diagnosis of a particular disease increases, the number of relevant diagnostic patterns among these variables increases. This increasing complexity decreases the clinician's ability to recognize patterns and accurately diagnose or predict disease.
Prostate cancer affects numerous individuals each year and many of them are killed by the disease. The early and accurate diagnosis of prostate cancer has been very difficult to achieve with reliability and accuracy. However, early diagnosis of prostate cancer is essential to maximizing the possibility of successfully treating the disease. Current screening techniques include digital rectal examination (DRE), transurethral prostatic biopsy, and measurement of prostate specific antigen (PSA) in the blood. Reliance on serum PSA levels, especially low PSA levels, as a sole diagnostic measure of prostate cancer often provides unacceptable levels of inaccurate diagnosis. These screening techniques miss many cases of early stage prostate cancer resulting in growth of the cancer within the prostate gland and also outside the capsule of the gland. It is essential to diagnose this disease in the early stages, well before metastases have occurred.
In addition, diagnostic methods should be capable of distinguishing between benign prostatic hyperplasia (BPH) and prostate cancer and to distinguish between cases of cancer and non-cancer. What is also needed is a valid, reliable, sensitive and accurate technique that can diagnose or prognose prostate cancer at an early stage and also distinguish the various stages of prostate cancer which can be characterized as T1b, T2, T3 and TN.times.M1.
Osteoporosis and osteopenia provide another example of disease with multiple biomarkers, the following biomarkers collectively show characteristic changes in the presence of osteoporosis: calcium, phosphate, estradiol (follicular, mid-cycle, luteal, or post-menopausal), progesterone (follicular, mid-cycle, luteal, mid-luteal, oral contraceptive, or over 60 years), alkaline phosphatase, percent liver-ALP, and total intestinal-ALP. After measuring these biomarkers, a diagnosing clinician would next compare the measurements to a normal reference range. While some of the biomarkers may fall outside the normal reference range, others may fall clearly within the normal reference range. In some circumstances, all of the biomarker values may fall within a normal reference range. Presented with such data, a clinician may suspect that a patient has undergone some bone loss, but will be unable to reach a conclusive and meaningful diagnosis as to the presence of the disease osteoporosis.
The characteristic changes in biomarkers associated with some diseases are well documented; however, the quantitative interpretation of each particular biomarker in diagnosing a disease and determining a prognosis is not well established. The difficulties inherent in formulating a diagnosis from the analysis of a set of laboratory data is best illustrated by looking closer at conventional diagnostic methods for a specific disease. A discussion of the disease osteoporosis follows.
The term "osteopenia" as used herein means any decrease in bone mass below the normal. The term "osteoporosis" as used herein means a specific form of generalized osteopenia characterized by a decrease in bone density, low bone mass, and microarchitectural deterioration of bone tissue.
Osteopenia encompasses a group of diseases with diverse etiologies typified by reduction in bone mass per unit volume to a level below that which is necessary for adequate mechanical support. Osteoporosis is the result of the gradual depletion of the inorganic portion of the skeleton and can be caused by any number of factors. Primary osteoporosis is an age related disorder that is particularly common in women and is characterized by decreased bone mass in the absence of other recognizable causes. However, osteoporosis occurs in both men and women. In women it is recognized usually at the 5.sup.th or 6.sup.th decade, following menopause. In men osteoporosis is often recognized around their 6.sup.th or 7.sup.th decade of life.
Several demographic parameters are associated with enhanced risk of developing osteoporosis. The following is a partial list of individuals whose demographics and behavior place them at risk for developing osteoporosis:
Post-menopausal women PA1 Cigarette smokers PA1 Heavy users of alcohol PA1 Users of a variety of drugs, such as steroids PA1 Female runners and ballet dancers PA1 Male marathoners consuming too few calories PA1 Bulemics and anorexics PA1 People with poor diets PA1 People allergic to dairy products PA1 People affected with cancer PA1 Fair and slim women PA1 All men and women over the age of 65. PA1 psychiatry (See Mulsant, B. H., "A Neural Network as an Approach to Clinical Diagnosis", MD Computing, Vol. 7, pp. 25-36 (1990)); PA1 autism (See Cohen, I., et al., "Diagnosing Autism: A Neural Net-Based Tool", PCAI, pp. 22-25 (May/Jun. 1994); pediatric radiology (See Boone, J. M., et al., "Neural Networks in Radiologic Diagnosis. I. Introduction and Illustration", Invest. Radiol., Vol. 25, pp. 1012-1016, (1990) and Gross, G. W., et al., "Neural Networks in Radiologic Diagnosis. II. Interpretation of Neonatal Chest Radiographs", Invest. Radiol., Vol. 25, pp. 1017-1023 (1990)); PA1 breast cancer (See Astion, M. L., et al., "Application of Neural Networks to the Interpretation of Laboratory Data in Cancer Diagnosis", Clin. Chem., Vol. 38, No. 1, pp. 34-38 (1992); Wus Y., et al., "Artificial Neural Networks in Mammography: Application to Decision Making in the Diagnosis of Breast Cancer", Radiology, Vol. 187, pp. 81-87 (1993); Kappen, H. J., et al., "Neural Network Analysis to Predict Treatment Outcome", Annals of Oncology, Vol. 4, Supp. 4, pp. S31-S34 (1993); and, Ravdin, P. M., et al., "A practical application of neural network analysis for predicting outcome of individual breast cancer patients", Breast Cancer Research and Treatment, Vol. 22, pp. 285-293 (1992)); PA1 ovarian cancer (See Wilding, P., et. al., "Application of backpropogation neural networks to diagnosis of breast and ovarian cancer", Cancer Letters, Vol. 77, pp. 145-153 (1994)). PA1 thyroid disease (See, Sharpe, P. K., et. al.; "Artifical Neural Networks in Diagnosis of Thyroid Function from in Vitro Laboratory Tests," Clin. Chem., Vol. 39, No. 11, pps. 2248-2253 (1993)); PA1 prostate cancer (See Snow, P. S. et al., "Artificial Neural Networks in the Diagnosis and Prognosis of Prostate Cancer: A Pilot Study" J. Urology, Vol. 152: 1923-1926 (1994)). PA1 cervical cancer (See U.S. Pat. No. 4,965,725 to Rutenberg); and, PA1 cardiology (See U.S. Pat. No. 5,280,792 to Leong et al. and Furlong, J. W., "Neural Network of Serial Cardiac Enzyme Data: A Clinical Application of Artifical Machine Intelligence", Am. J. Clin. Pathol, Vol. 96, No. 1, pp. 134-141 (July 1991).
In addition to being female, the three most significant risk factors are poor diet, lack of exercise, and being postmenopausal. Other risk factors which are associated with osteoporosis include racial factors such as Caucasian or Oriental ancestry, a fair complexion, and a family history of osteoporosis.
The onset of osteoporosis may be insidious or sudden, following trauma. The most common complaint associated with osteoporosis is back pain. Eventually, the pain may spread to the pelvis, the thorax, and the shoulders. In the spine, the vertebrae can compress, and the back can take on a "bent" appearance. Conditions such as kyphosis (humpback) or scoliosis may occur. If the spine becomes deformed, other body parts can be affected as well. For example, the ribs can be pushed against the pelvis, or the stomach can be pushed into the pelvis. In addition to spinal problems, osteoporosis can also lead to fractures of the hip, wrist, and ribs. These fractures can occur with only slight trauma and sometimes with no trauma at all. Mazess B., et al., "Bone Density of the Radius, Spine, and Proximal Femur in Osteoporosis," J. of Bone and Mineral Research, Vol. 3, pgs. 13-18, (1988); Riggs B. L., et al., "Involutional Osteoporosis", New Engl. J. Med., Vol. 314, pgs. 1676-1686, (1986). The changes associated with osteoporosis are gradual so osteoporosis is often not detected in its early stages.
Calcium and phosphorus are the main components of the inorganic portion of the skeleton. Chemical analysis of blood may reveal calcium, phosphorus, and alkaline phosphatase within the normal range. However, an isoenzyme of alkaline phosphatase may be significantly increased. Increased bone resorption seen in osteoporotic patients, which occurs as a result of the action of osteoclasts, usually involves the dissolution of both minerals and organic matrix eventually leading to increased excretion of urinary hydroxyproline. Serum estradiol which is secreted almost entirely by the ovary is significantly decreased in these patients.
An early decrease in bone mass can be measured by non-invasive assessment of the skeleton by four widely available methods that are known to those skilled in the art, including single photon absorptometry, dual photon absorptometry (DPA), dual-energy x-ray absorptometry (DXA), and quantitative computed tomography (CAT scan). Several of these methods are used to measure mineral content in the bone, and some are relatively selective for certain bones or trabecular versus cortical bone. These methods also provide different levels of radiation exposure.
Magnetic resonance imaging (MRI) and positron emission tomographic (PET) techniques may also reveal information useful in the diagnosis of various diseases including osteopenia and osteoporosis by providing information concerning bone density and vitality.
Radiographic absorptometry (RA) is a method for non-invasive measurement of bone mineral x-rays of the hand. Radiographs, taken with a standard x-ray machine, are sent to a central laboratory for computer-controlled analysis.
Current standard diagnostic techniques, are not effective for early detection of osteoporosis. Changes seen in osteoporosis are very gradual, and often go undetected in the early stages of the disease. Osteoporosis is often not detected in its early stages because bone mass must be decreased by about 30% to 40% before it is apparent using standard x-ray diagnostic techniques. Preventing osteoporosis by detecting early bone loss is far better than identifying the disease at relatively advanced stages and subsequently attempting to prevent its progression. Once major deterioration has occurred and gaps exist between the ends of fractured trabecular bone, no current treatment can be expected to restore the lost bone. Thus, therapeutic efforts must be directed toward prevention and early recognition of the progressive disease so treatment can be instituted before essentially irreversible structural damage ensues. Cummings S. R., et al., "Should Perimenopausal Women Be Screened for Osteoporosis?", Ann. Int. Med., Vol. 104, pgs. 817-823, (1986); Courpron P., "Bone Tissue Mechanisms Underlying Osteoporosis," Orthop. Clin. North Am., Vol. 12, pgs. 545, (1981); Frost H. M., "Mechanical Determinants of Bone Modeling," Metabol. Bone. Dis. Rel. Res., Vol. 4, pgs. 217-229, (1982). What is needed is a method for early detection and prediction of osteoporosis that considers the multiple biomarker and demographic variables associated with the disease.
One of the problems with the current methods for diagnosing osteoporosis is that the procedures do not give any information about the underlying cause of the osteoporosis, making it difficult to prescribe an appropriate course of treatment for the patient. For example, a common cause of postmenopausal osteoporosis is an estrogen deficit, which x-ray techniques cannot measure. Another problem inherent in the current diagnostic methods for osteopenia is that all of the current methods require expensive, sophisticated medical instrumentation to perform the bone density measurements. Additionally, patients must be exposed to x-rays. This makes a general screening of high risk populations impractical due to the expense and unavailability of the necessary instrumentation to the average clinic.
In view of the difficulties associated with extracting a diagnosis from the laboratory data for a set of predictive biomarkers, and also from demographic data optionally combined with biomarker data, there is need for automated diagnostic systems that are capable of complex pattern recognition. There have been several attempts at using computational models to achieve pattern recognition in diagnostics. One of the most popular computational methods for making diagnoses from multivariate laboratory data has been discriminate function analysis. However, diagnostic systems that rely exclusively on classical pattern recognition technology (geometric, syntactic, template, statistical) are not effective for evaluating the characteristic biomarker patterns of many disease states partially due to the inherent non-linear nature of the problem and a lack of known mathematical structure in the observed data. There is no clear set of rules that accurately describes how to analyze a set of biomarkers to reach a diagnosis.
In recent years, artificial neural networks have been gaining popularity as a means for recognizing and analyzing subtle diagnostic patterns in multivariate laboratory data. Neural networks possess the ability to discern patterns and trends too subtle or too complex for humans and conventional computational methods to identify. While humans can not easily assimilate more than two or three variables at once, neural networks can perceive correlations among hundreds of variables. Examples of areas in which neural networks have been explored for their value in clinical diagnosis and/or prognosis include:
Neural networks are capable of pattern recognition particularly suited to making diagnoses. Unlike current methods for arriving at a diagnosis from a logical set of rules, neural networks do not require explicit encoding of process knowledge in a set of rules. Neural networks learn from examples. Neural networks learn more efficiently when the data to be input into the neural network is preprocessed.
There are two basic approaches in computer assisted clinical pattern classification techniques. The first approach applies known knowledge and facts (physiological, anatomical, molecular biological, etc.) of a given disease process and attempts to establish links between observed or measured data and one of several possible classification classes. Such existing knowledge and facts are often expressed as rules (e.g. clinical expert systems), certain forms of numerical functions (e.g. statistical distributions in parametric statistical inferences), or even complex models that can only be described with systems of equations (e.g. pharmacokinetic models).
The second approach uses numerical procedures to adaptively construct and modify a numerical classification system based on available training data which are essentially sets of input values paired with known classification results. In this approach, the human expert knowledge is not or can not be expressed in an explicit form. Instead, the knowledge is implicitly provided in the training data with confirmed classifications. The extraction of such knowledge through supervised learning (learning from examples) and the adaptive construction of the classification system are left entirely to the learning algorithm. Classification systems with this second approach include various forms of neural network classifiers such as Multilayer Feedforward Perceptrons.
Both approaches have their shortcomings. The first approach uses explicit knowledge in the subject area to associate observed unknown data with a known class. However, in many practical situations, such knowledge is incomplete, or a portion of it cannot be expressed in explicit and precise terms, so that it can be directly coded into the classification system. On the other hand, the pure numerical pattern classification approach places the burden of constructing the classification system entirely to the adaptive learning process. The performance of the obtained system is limited to the amount and extent of information contained in the training data and the effectiveness of the learning algorithm in extracting such information, despite the fact that there may exist a tremendous amount of prior knowledge about the subject area. In some cases where there is no preprocessing such as preselection or scaling of the patient data, the training of a neural network may be extremely difficult if not impossible since the number of input variables may be too large and the relationship of these variables to a specific disease may be too weak to achieve the desired predictive accuracy.
Accordingly what is needed is an approach to diagnosing and prognosing disease that incorporates an apparatus and a system capable of accommodating a large number of factors, such as biomarker and demographic factors. This system should be capable of processing a large number of patients and patient variables such as biomarker and demographic factors. This approach to diagnosis and prognosis of disease should select factors with high predictive values, preprocess these factors, and input the data into a computer-based neural network or multiple neural networks in order to train the neural network(s) to predict or diagnose disease. These neural network(s) should produce a diagnostic index comprised of one or several output values indicative of the presence (diagnosis) or future occurrence (prognosis) of a disease. The system should possess the capacity to input patient data into the trained neural network and produce an output value to indicate if the patient has or will have the disease.
Furthermore, since clinicians will rarely have such computer-based neural network capabilities at their disposal, what is also needed is a system whereby patient data can be transmitted to a computer-based neural network as described above, which will receive the data, input it into the trained neural network, produce an output value indicative of a diagnosis or prognosis and then transmit the information concerning the diagnosis or prognosis to another location, such as the originating data transmitting station, or perhaps directly to the clinician's office. Such a system would provide access to sophisticated and highly trained prognostic and diagnostic neural networks which would enhance the accuracy of clinician's diagnostic and prognostic capability. This system should be capable of receiving high volumes of patient data and rapidly processing the data through the neural networks to obtain diagnoses and prognoses of disease.
Such a system could be used for diagnosis and prognosis of any disease or condition for which a neural network may be specifically trained.